Ronghui You
YOU?
Author Swipe
Atypical presentation of pseudohypoparathyroidism with absence of mutations in the GNAS gene: a case report Open
Not applicable.
LiD-FL: Towards List-Decodable Federated Learning Open
Federated learning is often used in environments with many unverified participants. Therefore, federated learning under adversarial attacks receives significant attention. This paper proposes an algorithmic framework for list-decodable fed…
LiD-FL: Towards List-Decodable Federated Learning Open
Federated learning is often used in environments with many unverified participants. Therefore, federated learning under adversarial attacks receives significant attention. This paper proposes an algorithmic framework for list-decodable fed…
Near-Optimal Resilient Aggregation Rules for Distributed Learning Using 1-Center and 1-Mean Clustering with Outliers Open
Byzantine machine learning has garnered considerable attention in light of the unpredictable faults that can occur in large-scale distributed learning systems. The key to secure resilience against Byzantine machines in distributed learning…
Structure-aware protein sequence alignment using contrastive learning Open
Protein alignment is a critical process in bioinformatics and molecular biology. Despite structure-based alignment methods being able to achieve desirable performance, only a very small number of structures are available among the vast of …
Near-Optimal Resilient Aggregation Rules for Distributed Learning Using 1-Center and 1-Mean Clustering with Outliers Open
Byzantine machine learning has garnered considerable attention in light of the unpredictable faults that can occur in large-scale distributed learning systems. The key to secure resilience against Byzantine machines in distributed learning…
DeepMHCI: an anchor position-aware deep interaction model for accurate MHC-I peptide binding affinity prediction Open
Motivation Computationally predicting major histocompatibility complex class I (MHC-I) peptide binding affinity is an important problem in immunological bioinformatics, which is also crucial for the identification of neoantigens for person…
Protein language model powers accurate and fast sequence search for remote homology Open
Homologous protein search is one of the most commonly used methods for protein annotation and analysis. Compared to structure search, detecting distant evolutionary relationships from sequences alone remains challenging. Here we propose PL…
Protein language model powers accurate and fast sequence search for remote homology Open
Homologous protein search is one of the most commonly used methods for protein annotation and analysis. Compared to structure search, detecting distant evolutionary relationships from sequences alone remains challenging. Here we propose PL…
NetGO 3.0: Protein Language Model Improves Large-Scale Functional Annotations Open
As one of the state-of-the-art automated function prediction (AFP) methods, NetGO 2.0 integrates multi-source information to improve the performance. However, it mainly utilizes the proteins with experimentally supported functional annotat…
MetaBinner: a high-performance and stand-alone ensemble binning method to recover individual genomes from complex microbial communities Open
Binning aims to recover microbial genomes from metagenomic data. For complex metagenomic communities, the available binning methods are far from satisfactory, which usually do not fully use different types of features and important biologi…
NetGO 3.0: Protein Language Model Improves Large-scale Functional Annotations Open
As one of the state-of-the-art automated function prediction (AFP) methods, NetGO 2.0 integrates multi-source information to improve the performance. However, it mainly utilizes the proteins with experimentally supported functional annotat…
DeepMHCII: a novel binding core-aware deep interaction model for accurate MHC-II peptide binding affinity prediction Open
Motivation Computationally predicting major histocompatibility complex (MHC)-peptide binding affinity is an important problem in immunological bioinformatics. Recent cutting-edge deep learning-based methods for this problem are unable to a…
DeepMHCII: A Novel Binding Core-Aware Deep Interaction Model for Accurate MHC II-peptide Binding Affinity Prediction Open
Computationally predicting MHC-peptide binding affinity is an important problem in immunological bioinformatics. Recent cutting-edge deep learning-based methods for this problem are unable to achieve satisfactory performance for MHC class …
MetaBinner: a high-performance and stand-alone ensemble binning method to recover individual genomes from complex microbial communities Open
Binning is an essential procedure during metagenomic data analysis. However, the available individual binning methods usually do not simultaneously fully use different features or biological information. Furthermore, it is challenging to i…
View article: Critical Assessment of Metagenome Interpretation - the second round of challenges
Critical Assessment of Metagenome Interpretation - the second round of challenges Open
Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the community-driven initiative for the Critical Assessment of Metagenome Interpretation (CAMI). In its second challenge, CAMI engaged the communi…
ARG-SHINE: improve antibiotic resistance class prediction by integrating sequence homology, functional information and deep convolutional neural network Open
Antibiotic resistance in bacteria limits the effect of corresponding antibiotics, and the classification of antibiotic resistance genes (ARGs) is important for the treatment of bacterial infections and for understanding the dynamics of mic…
NetGO 2.0: improving large-scale protein function prediction with massive sequence, text, domain, family and network information Open
With the explosive growth of protein sequences, large-scale automated protein function prediction (AFP) is becoming challenging. A protein is usually associated with dozens of gene ontology (GO) terms. Therefore, AFP is regarded as a probl…
DeepGraphGO: graph neural network for large-scale, multispecies protein function prediction Open
Motivation Automated function prediction (AFP) of proteins is a large-scale multi-label classification problem. Two limitations of most network-based methods for AFP are (i) a single model must be trained for each species and (ii) protein …
BERTMeSH: deep contextual representation learning for large-scale high-performance MeSH indexing with full text Open
Motivation With the rapid increase of biomedical articles, large-scale automatic Medical Subject Headings (MeSH) indexing has become increasingly important. FullMeSH, the only method for large-scale MeSH indexing with full text, suffers fr…
BERTMeSH: Deep Contextual Representation Learning for Large-scale High-performance MeSH Indexing with Full Text Open
Motivation With the rapid increase of biomedical articles, large-scale automatic Medical Subject Headings (MeSH) indexing has become increasingly important. FullMeSH, the only method for large-scale MeSH indexing with full text, suffers fr…
FullMeSH: improving large-scale MeSH indexing with full text Open
Motivation With the rapidly growing biomedical literature, automatically indexing biomedical articles by Medical Subject Heading (MeSH), namely MeSH indexing, has become increasingly important for facilitating hypothesis generation and kno…
NetGO: improving large-scale protein function prediction with massive network information Open
Automated function prediction (AFP) of proteins is of great significance in biology. AFP can be regarded as a problem of the large-scale multi-label classification where a protein can be associated with multiple gene ontology terms as its …
HAXMLNet: Hierarchical Attention Network for Extreme Multi-Label Text\n Classification Open
Extreme multi-label text classification (XMTC) addresses the problem of\ntagging each text with the most relevant labels from an extreme-scale label\nset. Traditional methods use bag-of-words (BOW) representations without context\ninformat…